The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for determining the appropriate medical image processing pipeline to be used to process medical images based on machine learning applied to medical image data and/or corresponding textual data.
Various medical imaging technologies have been developed to assist medical professionals in obtaining an understanding of a patient's anatomy and internal medical condition in a non-invasive manner or minimally invasive manner. For example, these medical imaging technologies include X-ray technology, computed tomography (CT) scan technology, magnetic resonance imaging (MRI) technology, positron emission tomography (PET) scan technology, sonography technology, etc. Medical images captured by such technology may have various different characteristics including the particular modality used, the mode used, the view, and the particular portion of the human body for which the medical images are captured.
Recently, there has been interest in providing computer aided mechanisms to assist with the analysis of such captured medical images, so as to alleviate the burden on human beings. For example, medical image segmentation, registration, and classification algorithms have been developed for analyzing the medical images and identifying anatomical structures present within the medical images. However, such algorithms are fixed algorithms that only operate on a specific type of medical imaging technology or specific characteristic of a collection of medical images being analyzed, e.g., only medical images captured using a specific mode or view.